Schools use targeted interventions to help students at risk of school dropout or late graduation. Identifying these students takes significant investment in people, process, and technology, and warning signs are often context-specific and scattered across data sources. Extremely high counselor caseloads compound the problem, and are worse in schools serving children with other structural disadvantages. We describe how machine learning technology can improve existing educational systems like the Minnesota Early Indicator and Response System, and provide details on this new technology's statewide implementation in Kentucky. We discuss key technical challenges for early warning data systems and best practices for overcoming them. These challenges specifically include foregrounding fairness, accountability, and transparency, and we offer a discussion of how computational public policy systems can mitigate or worsen equitable access for all students.
Head of learning science technology, Infinite Campus
Data scientist with a decade of experience in education data, software engineering, and product management, with a focus on recommender and decision support systems.
Thomas Christie is a data scientist at Infinite Campus, a student information system for 8 million students across the United States. At Infinite Campus, Thomas applies state-of-the-art machine learning techniques to solve problems in education. Thomas is also graduate student... Read More →
Thursday May 30, 2019 1:15pm - 2:00pm CDT
(F) P0838Normandale Partnership Center, 9700 France Ave So, Bloomington, MN 55431
